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Modeling the ferroelectric phase transition in barium titanate with DFT accuracy and converged sampling

Lorenzo Gigli1*, Alexander Goscinski1*, Michele Ceriotti1*, Gareth A. Tribello2*

1 Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland

2 Centre for Quantum Materials and Technologies (CQMT), School of Mathematics and Physics, Queen’s University Belfast, Belfast, BT7 1NN

* Corresponding authors emails: cangelsi@hotmail.it, alex.goscinski@epfl.ch, michele.ceriotti@epfl.ch, g.tribello@qub.ac.uk
DOI10.24435/materialscloud:xw-g5 [version v1]

Publication date: Apr 10, 2024

How to cite this record

Lorenzo Gigli, Alexander Goscinski, Michele Ceriotti, Gareth A. Tribello, Modeling the ferroelectric phase transition in barium titanate with DFT accuracy and converged sampling, Materials Cloud Archive 2024.54 (2024), https://doi.org/10.24435/materialscloud:xw-g5


The accurate description of the structural and thermodynamic properties of ferroelectrics has been one of the most remarkable achievements of Density Functional Theory (DFT). However, running large simulation cells with DFT is computationally demanding, while simulations of small cells are often plagued with non-physical effects that are a consequence of the system's finite size. Therefore, one is often forced to use empirical models that describe the physics of the material in terms of effective interaction terms, that are fitted using the results from DFT, to perform simulations that do not suffer from finite size effects. In this study we use a machine-learning (ML) potential trained on DFT, in combination with accelerated sampling techniques, to converge the thermodynamic properties of Barium Titanate (BTO) with first-principles accuracy and a full atomistic description. Our results indicate that the predicted Curie temperature depends strongly on the choice of DFT functional and system size, due to the presence of emergent long-range directional correlations in the local dipole fluctuations. Our findings demonstrate how the combination of ML models and traditional bottom-up modeling allow one to investigate emergent phenomena with the accuracy of first-principles calculations and the large size and time scales afforded by empirical models.

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45.8 MiB This MaterialsCloud archive provides the datasets and Machine Learning models needed to run the calculations and reproduce the results and figures in the manuscript "Modeling the ferroelectric phase transition in barium titanate with DFT accuracy and converged sampling", by L. Gigli, A. Goscinski, M. Ceriotti, G. A. Tribello, arXiv:2310.12579 [cond-mat.mtrl-sci], 2023.


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Ferroelectrics Finite-size effects Metadynamics Phase transitions Machine Learning potentials hybrid-DFT ML Dielectric correlations MARVEL SNSF Sinergia

Version history:

2024.54 (version v1) [This version] Apr 10, 2024 DOI10.24435/materialscloud:xw-g5